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Nowadays, with the continuous expansion of application scenarios of roboticarms, there are more and more scenarios where nonspecialist come into contactwith robotic arms. However, in terms of robotic arm visual servoing,traditional Position-based Visual Servoing (PBVS) requires a lot of calibrationwork, which is challenging for the nonspecialist to cope with. To cope withthis situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees peoplefrom tedious calibration work. This work applied a model-free adaptive control(MFAC) method which means that the parameters of controller are updated in realtime, bringing better ability of suppression changes of system and environment.An artificial intelligent neural network is applied in designs of controllerand estimator for hand-eye relationship. The neural network is updated with theknowledge of the system input and output information in MFAC method. Inspiredby "predictive model" and "receding-horizon" in Model Predictive Control (MPC)method and introducing similar structures into our algorithm, we realizes theuncalibrated visual servoing for both stationary targets and movingtrajectories. Simulated experiments with a robotic manipulator will be carriedout to validate the proposed algorithm.

A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network